# 导入必要的库和模块
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import pickle
from tqdm import tqdm

# 加载数字数据集
digits = load_digits()
X, y = digits.data, digits.target

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_acc = 0
best_k = 1
best_knn = None

# 初始化一个列表以存储每个k值的准确率
acc_list = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41), desc="Searching for best k"):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    y_pred = knn.predict(X_test)
    acc = accuracy_score(y_test, y_pred)
    acc_list.append(acc)
    if acc > best_acc:
        best_acc = acc
        best_k = k
        best_knn = knn

# 将最佳KNN模型保存到二进制文件
with open("best_knn_model.pkl", "wb") as f:
    pickle.dump(best_knn, f)

# 打印最佳准确率和相应的k值
print(f"最优准确率: {best_acc*100:.2f}%  最优K值: {best_k}")

# 绘制准确率随k变化的折线图
plt.figure(figsize=(8, 6))
plt.plot(range(1, 41), acc_list, marker='o', label='Accuracy')
plt.axvline(x=best_k, color='red', linestyle='--', label=f'Best k = {best_k}')
plt.scatter([best_k], [best_acc], color='red')
plt.text(best_k, best_acc, f'k={best_k}\nacc={best_acc*100:.2f}%', color='red', ha='left', va='bottom')
plt.xlabel('k')
plt.ylabel('Accuracy')
plt.title('KNN Accuracy for Different k')
plt.legend()
plt.tight_layout()
plt.savefig('accuracy_plot.pdf')
plt.close()